Summary: weekly meeting 20160706 (Byron, Matt, me) - mobeets/nullSpaceControl GitHub Wiki

get figures down detailed outline of various sections

methods before or after results:

  • nature neuro methods is after
  • neuron -- ?

any time you have a "we found" that's results, not discussion

  • and add it in supplemental figures

target audience: anyone at cosyne, or experimentally-oriented neuroscience

  • want high-level idea--easy to skim!
  • should easily be able to find a big-picture question answered in the results

main result first: e.g. figure 3

  • also that one figure with all results (x-axis is session, y-axis is mean error)
    • but connecting dots with a line doesn't make sense...find a right way to do it then go into high-level explanation (rather than zooming in)
  • e.g., unconstrained cartoon, put the blue line lower and then you'd predict null space activity outside the "cloud"
  • but maybe find a way to show real data

supplemental only if you can't work it into the main story

need to provide criteria for using certain data sets, if i don't use all of them

don't have pruning in first round, or mean shift

  • make them maybe figure 8
  • otherwise they cloud the story since they aren't better

for dates:

  • preface dates with "J" or "L" in names so it's clear which data set is which monkey

IME and task details are in methods

use of "null space": this is okay

  • maybe in introduction introduce y = f(Bx) where B is short and fat, give explanation for how there are multiple x's that produce the same y [or it could just be y = f(x), and in our case f(x) = Bx]
  • we don't know f or B in muscles, but with BCI we can define this relationship

factor analysis makes assumption that all activity outside of 10d space is private noise unrelated to the task

  • need high-level motivation for factor analysis: if you look at one neuron, some variability is just due to it spiking; but FA will return the shared part, more likely to be involved in the task?
  • n.b. FA does not return orthogonal axes
    • check out HW toy problem with FA that illustrates this point
  • could assess this in spike space easily enough, but estimates might be much noisier (e.g., estimating covariance in high-d with low numbers of points)

should i cover hypothesis performance as a function of learning?

  • that's more in matt's domain...

maybe start with intuitive fitting intuitive

  • this will knock out unconstrained, minimal energy
  • habitual and cloud are very close but wait! maybe this is just because there's no learning. so now we introduce the perturbation...
  • show that habitual does much worse now
  • can maybe use IME-only in this case also motivation for using IME only in the perturbation case
  • in intuitive, he's killing it
  • in perturbation, he learns but never recovers--so we need to estimate his internal model